Judges’ Queries and Presenter’s Replies

What is the evidence for the distinction you make between a planned and unplanned movement? It is a little puzzling that a movement could be executed without some kind of plan, so I’m wondering whether the “plan state” might represent some different stages of preparing a movement that are somehow optional and can be skipped if a sudden movement is required.

Great question, and certainly one that we have been thinking over a lot. We have a few data points which I think can be useful here:

1. The distinction that we make between a planned and unplanned movement is that “planned” movements have a delay between deciding what movement to make and beginning to execute that movement. The task that we use is a delayed reaching task, in which subjects are told what target to reach to but must withhold from making the movement until a subsequent “go cue” is given. What we find is that with a delay, the amount of time that passes between the go cue and the arm beginning to move (reaction time) is significantly shorter than if subjects are not given a delay period. During the delay between the target onset and the go cue, we also observe neural activity moving to this putative preparatory state. However, the reaction time savings are only on the order of ~30 ms, but it takes ~200 ms of delay to achieve this benefit. So it could be that this state is a helpful but not necessary “optimization” procedure – if you have time to get there, it is beneficial to do so, but it would be silly to take 200 ms to save 30 ms if you don’t have much time – overall, you’d be losing ~170 ms! We found that this is indeed the case, and that just as you suggested, “planning” is indeed optional and can be skipped when a sudden movement is required. We were very surprised by our results because many in the field had previously thought that this planning was a necessary step before making a reach, and that the requirement to first achieve the plan state was why unplanned movements took longer to initiate.

2. Because the plan state is achieved during a delay period in which subjects must withhold from moving, it is probably more properly thought of as a “prepare and hold” state. So it could be the case that this state corresponds to the need to simultaneously prepare one reach but not execute it, whereas the neural activity might be less constrained if there is no need to refrain from moving while preparing an upcoming reach.

1. We performed experiment in 2 monkeys across several datasets of 100+ neurons, acquiring a quantity of data which is considered standard in the field.
2. Furthermore, we tested our hypothesis using two distinct behaviors: delayed reaching vs. non-delayed reaching, and a “switch” task in which the pre-cued target moved locations, thus examining neural activity which has been “correctly” planned, “unplanned”, or “incorrectly” planned at the time of the go cue (certainly it would be possible to perform the correct planning after the go cue but before movement onset, which is what we were trying to test). The fact that our results generalize across reaching tasks leads us to be more confident in our results.
3. It is worth noting, however, that the task we are studying is highly constrained: monkeys sitting still in a chair touching targets on a screen which only move in a 2-dimensional environment. While we certainly hope that the principles we are examining here generalize to a less constrained, freely-moving type of environment, it is of course always possible that the effects we are studying will not fully generalize in all cases. We’d like to note that we explicitly tried to use more varied behavioral tasks explicitly because many studies of motor planning and execution do not explicitly compare different delay period lengths, and we wanted to see what principles were able to generalize.

Thank you.
In reference to Eileen’s question, you may wish to look at work on choice and decision making, which discusses System 1 and System 2. That framework is different than ours, but I think there could be some some interesting parallels.

Good question-
Yes, this is something that we actively wanted to test in this study. During a delay period, neural activity achieves a very particular set of firing rates which is usually referred to as “plan” activity. But it had never been examined whether this state is achieved in the absence of a delay period. Certainly it is possible for monkeys to make a movement without a delay period, but in such cases, the monkey takes longer to make the reach. One hypothesis, before this study, was that the extra time in the no-delay case was required so that the neural activity could achieve the plan state just before making the movement. What we determined is that, in the absence of a delay period, neural activity never achieves the exact set of firing rates which had previously been thought of as the signature of neural planning, thus suggesting that the “plan” that we observe during the delay is fundamentally different from the neural activity in non-delayed reach trials. This means that we need to fundamentally re-examine (as you suggest) what the nature or definition of motor planning is, in order to account for the fact that we get activity which is very specific to the exact nature of task timing.

In the video there is a person drinking using a prosthetic while in a bed, what progress has been made in the use of personal/mobile neural prosthetics that might allow a person to leave a hospital bed?

The current human clinical trials for intracortical neural prostheses, including the ones we are developing, have been limited to those patients for whom the potential benefit most outweighs the risk of a novel intracortical therapy; for now this has limited it to individuals with tetraplegia. These users typically control their clinical trial (IDE) devices from their home bed or wheelchair. However, as the technology progresses we envision it being used to restore movement to patients for a variety of uses, most of which would take them beyond the hospital bed.

Specifically,

1.) Although current devices used a wired connector from the multielectrode arrays to the processing unit as seen in our video, fully implantable wireless systems have been developed and are currently undergoing safety testing. See for example [Borton et al., An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. Journal of Neural Engineering, 2013].

2.) Our group conducted interviews with a number of patients who could potentially benefit from this technology, and found that being able to communicate (for patients who are unable to speak) and regain reach and grasp function are amongst the most desired quality of life improvement for most individuals with paralysis [Blabe et al., Assessing brain-machine interface priorities from the perspective of spinal cord injury participants. Poster at the Society for Neuroscience Annual Conference, 2012]. Hence, we are trying to restore these functions first.

3.) Once we can accomplish dextrous control of a robot arm, it is a comparably small step to mount said arm on a mobile wheelchair which could also itself be under neural control. This would allow a paralyzed individual to interact with their environment while being mobile.

4.) Furthermore, a neural prosthetic that accurately decodes arm movement intention can be used to drive a prosthetic arm worn by someone who is missing their own arm. This is an especially important population segment given the number of such injuries amongst military veterans recently returned from Iraq and Afghanistan.

5.) For some patients who remain in a hospital bed, control of a robot arm is not their first priority. As I mentioned, many such patients listed the ability to communicate as a top priority. Therefore, in addition to studying the neuroscience of arm reaches, we’ve also been developing algorithms to quickly decode a user’s intention to drive a cursor on a computer screen; this would allow locked-in patients to communicate using text-to-speech software, access the internet, and control their environment. To this end, our group has recently developed a decoding algorithm [Gilja, Nuyujukian, et al., A high-performance neural prosthesis enabled by control algorithm design. Nature neuroscience 2012] which allowed monkeys to perform a typing-like task at a rate of up to 13 words per minute [Nuyujukian et al., A high-performance, robust brain-machine interface without retraining. Poster at Computational and Systems Neuroscience 2012].

6.) Other groups have made progress towards decoding neural activity underlying walking. See, for instance, [Fitzsimmons et al., Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity. Front. Integrative Neuroscience, 2009]. It may someday be possible to help a paralyzed individual walk either by using their neural activity to drive a robotic exoskeleton, or by stimulating the muscles of their legs using functional electrical stimulation.

You seem to assume that all planning occurs before action, however, a number of models of planning assume that modification during plan execution can, and in fact may often, occur, e.g. the Hayes-Roth Opportunistic Planning Model. Do you think your definition of planning is too restrictive, especially given the type of planning that we know occurs in more open problem situations?

Thanks for the question! You raise an excellent point. In these experiments, we focused on motor planning that occurs before movement is initiated. The experiments performed in this study seek to address the specific hypothesis that the particular pattern of firing rates achieved by motor cortical neurons during delayed reaches represents the completion of a planning process, which must necessarily be achieved before movement occurs. Using two different behavioral tasks, we showed that this “plan state” is not necessary. As you pointed out, the neural activity we’re focusing on here occurs before the movement is initiated.

However, I completely agree with your view that aspects of planning can and do take place later in the trial, even after movement begins. We know both from experience and behavioral observations in psychophysics experiments that the motor system makes use of a wide variety of feedback from the visual system, from proprioception and somatosensation, and from the vestibular system in order to guide accurate movements as they happen. And at a more cognitive level, we also continuously re-evaluate the movement we are executing in order to ensure that it will still accomplish our current behavioral goal in our dynamic environment. This information feeds into the motor system from numerous other brain regions. Our lab and others are engaged in new experiments trying to elucidate the neural mechanisms by which this diverse information is integrated, both before and after movement is initiated.

We imagine that a few factors are likely at play. First, there may be a degree of variability in the urgency of the adjustment. In our task, because the delay period was variable and the monkey could be asked to make a reach at any time, we tried to keep a “high urgency” to make sure that the monkeys had an incentive to be actively prepared to reach at any time. However, in other circumstances, there may be less urgency and this might increase the amount of time to adjust. Second, the “informativeness” of the goal change could also be important. Because we only had a single target visible at any given time, and the correct response was clear, there were presumably not too many decision-making processes which needed to be engaged. But it has been shown previously that neurons in motor cortex can show “partial” preparatory responses if more than one target is shown, indicating that planning may not be an all-or-none process (e.g. Cisek and Kalaska, Neuron 2005). In a less constrained task, new information could come in which changes the probability of a given response being correct without being definitive one way or another. This could certainly affect the rate of adjustment. Finally, we only examined the effects of visual inputs in this study, but different sensory modalities could also be transmitted at different latencies. There are a lot of direct connections between primary somatosensory (touch and proprioception) cortex and motor cortex, so transmitting new information in this way could lead to a faster response as well.

Great video. You made neural planning quite comprehensible. Thanks! In terms of implications for prosthetic devices you might be interested in a somewhat related video:http://posterhall.org/igert2013/posters/376
Thanks for sharing your very interesting work!!!

mkrmapalagama

Jauher Zaidi

Guest

May 22, 2013 | 09:28 p.m.

Amazing! This is a great research. My company Netvinci is working on machine vision. The more we work on machine vision. I am amazed how our brain do parallel processing for video, sound and make decision. In real time. I would like to see this research help people in the near future.

Thanks, Edinah. In response to your question: our group has focused primarily on studying arm reaching, and so we record from dorsal premotor cortex and the arm area of primary motor cortex; thus, we don’t have data for activities such as speech and eye movements. These are both activities for which there are specialized brain regions, and so it’s possible that rather different neuronal dynamics exist for them compared to the neural dynamics during reach planning and execution. For instance, the topographical organization of neurons in the frontal eye field, and the relatively simple population code for saccade direction encoded by this population, makes me hesitate to predict that the planning dynamics there would like what we report for reaching.

To really find out the answer, we’ll have to wait until someone applies our analytical method to neural recordings made during these types of movements.

Hi Cindy, thanks for the question. Right now these are being tested for safety in a small number of participants in clinical trials. Although the technology is advancing, it’s not yet ready for widespread clinical use — and that’s why we and many other groups are doing the relevant research to close this gap. As for what determines suitability, for the current clinical trials the criteria include tetraplegia, otherwise being healthy, and living close enough to one of the clinical trial sites.

Neural Dynamics of Reaching Following Incorrect or Absent Motor Planning

Neural prostheses are biomedical devices for restoring movement to individuals with paralysis or lost limbs. Signals from the patient’s motor cortex are decoded to infer intended movements, allowing the user to naturally control a prosthetic arm. Current neural prostheses assume that the observed neural activity relates only to the immediate movement being controlled. This assumption precludes doing more complex tasks such as planning out a movement or simultaneously executing one action while planning a subsequent one. To enable next-generation neural prostheses, we set out to understand how movement planning relates to movement execution. When given time to plan a reach, neural activity achieves and holds a distinct pattern of firing rates, called the plan state, which is specific to the upcoming movement. The plan is beneficial for initiating the reach quickly. It remains unknown, however, whether entering this plan state is necessary for successfully generating the reach. Our experiments answer this fundamental question by testing whether the neural state must pass through the plan state before a movement is generated in two cases: 1) there is no time to plan, and 2) a different movement has already been planned. We recorded from premotor and primary motor cortices of macaques making reaches in these two situations. In both cases, we found that although the monkey’s reaction time was slower, the movement was the same even though neural activity did not pass through the plan state. Thus, motor planning is a beneficial, but not strictly necessary, step of movement generation.